An adaptive functional regression-based prognostic model for applications with missing data

Xiaolei Fang, Rensheng Zhou and Nagi Gebraeel

Reliability Engineering and System Safety, 2015, vol. 133, issue C, 266-274

Abstract: Most prognostic degradation models rely on a relatively accurate and comprehensive database of historical degradation signals. Typically, these signals are used to identify suitable degradation trends that are useful for predicting lifetime. In many real-world applications, these degradation signals are usually incomplete, i.e., contain missing observations. Often the amount of missing data compromises the ability to identify a suitable parametric degradation model. This paper addresses this problem by developing a semi-parametric approach that can be used to predict the remaining lifetime of partially degraded systems. First, key signal features are identified by applying Functional Principal Components Analysis (FPCA) to the available historical data. Next, an adaptive functional regression model is used to model the extracted signal features and the corresponding times-to-failure. The model is then used to predict remaining lifetimes and to update these predictions using real-time signals observed from fielded components. Results show that the proposed approach is relatively robust to significant levels of missing data. The performance of the model is evaluated and shown to provide significantly accurate predictions of residual lifetime using two case studies.

Keywords: Condition monitoring; Prognostics; Functional principal components analysis; Functional regression analysis; Remaining useful life (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:133:y:2015:i:c:p:266-274

DOI: 10.1016/j.ress.2014.08.013

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